detAI23

detAI23 is a modular open-source artificial intelligence framework designed for detection-oriented machine learning tasks. It provides a suite of tools for constructing, training, evaluating, and deploying models focused on anomaly detection, event detection, and pattern recognition across domains such as cybersecurity, industrial monitoring, and healthcare. The framework emphasizes interoperability, explainability, and governance, offering a pluggable architecture that can integrate diverse data sources and model types.

Development of detAI23 began around 2023 as a collaborative effort among researchers and practitioners from multiple institutions. The project follows an open-development model, with ongoing contributions, documentation, and community-driven improvements. The name reflects its core focus on detection capabilities in AI systems.

Its architecture centers on a modular core that coordinates data ingestion, preprocessing, feature extraction, and model orchestration. A model zoo provides a range of detection algorithms, including supervised, semi-supervised, and unsupervised methods. The framework supports privacy-preserving options such as federated learning and differential privacy, and includes an explainability layer, model health monitoring, and governance features to track performance and compliance.

Common use cases include network intrusion and fraud detection in cybersecurity, fault and anomaly monitoring in industrial environments, and early-warning systems in healthcare. Users can deploy pipelines in real time or batch modes, with support for streaming data, visualization, and auditable decision trails.

DetAI23 has seen adoption in academic experiments and pilot deployments across sectors. It is praised for its modularity, transparency, and extensibility, though it can require significant compute resources and careful data governance to achieve robust results.